

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_   							=  get_tests_dir('fixtures/test_sentencepiece.model')
lowercase_   							=  get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase_   							=  'pt' if is_torch_available() else 'tf'





@require_sentencepiece
@require_tokenizers
class 					__lowerCAmelCase	(   __lowercase     ,     unittest.TestCase				):
  _a		     =    CamembertTokenizer
  _a		     =    CamembertTokenizerFast
  _a		     =    True
  _a		     =    True




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        super().setUp()

        # We have a SentencePiece fixture for testing
        _lowercase      			=CamembertTokenizer(lowerCAmelCase   )
        tokenizer.save_pretrained(self.tmpdirname   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			="<pad>"
        _lowercase      			=1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase   )				,  lowerCAmelCase   )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase   )				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=list(self.get_tokenizer().get_vocab().keys()   )

        self.assertEqual(vocab_keys[0]				,  '<s>NOTUSED'   )
        self.assertEqual(vocab_keys[1]				,  '<pad>'   )
        self.assertEqual(vocab_keys[-1]				,  '<mask>'   )
        self.assertEqual(len(lowerCAmelCase   )				,  1_004   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        self.assertEqual(self.get_tokenizer().vocab_size				,  1_005   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=CamembertTokenizer(lowerCAmelCase   )
        tokenizer.save_pretrained(self.tmpdirname   )
        _lowercase      			=CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

        _lowercase      			="I was born in 92000, and this is falsé."

        _lowercase      			=tokenizer.encode(lowerCAmelCase   )
        _lowercase      			=rust_tokenizer.encode(lowerCAmelCase   )
        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )

        _lowercase      			=tokenizer.encode(lowerCAmelCase				,  add_special_tokens=lowerCAmelCase   )
        _lowercase      			=rust_tokenizer.encode(lowerCAmelCase				,  add_special_tokens=lowerCAmelCase   )
        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        _lowercase      			=tokenizer.convert_ids_to_tokens(lowerCAmelCase   )
        _lowercase      			=rust_tokenizer.tokenize(lowerCAmelCase   )
        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        if not self.test_rust_tokenizer:
              return

        _lowercase      			=self.get_tokenizer()
        _lowercase      			=self.get_rust_tokenizer()

        _lowercase      			="I was born in 92000, and this is falsé."

        _lowercase      			=tokenizer.tokenize(lowerCAmelCase   )
        _lowercase      			=rust_tokenizer.tokenize(lowerCAmelCase   )
        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )

        _lowercase      			=tokenizer.encode(lowerCAmelCase				,  add_special_tokens=lowerCAmelCase   )
        _lowercase      			=rust_tokenizer.encode(lowerCAmelCase				,  add_special_tokens=lowerCAmelCase   )
        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )

        _lowercase      			=self.get_rust_tokenizer()
        _lowercase      			=tokenizer.encode(lowerCAmelCase   )
        _lowercase      			=rust_tokenizer.encode(lowerCAmelCase   )
        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )




  @slow
  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			={"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        _lowercase      			=[
            "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
            "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
            "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
            "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
            "telles que la traduction et la synthèse de texte.",
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCAmelCase				,  model_name='camembert-base'				,  revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'				,  sequences=lowerCAmelCase				,  )




from __future__ import annotations




class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:str	,  a						:str		)						->					Union[str, Any]:
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   text, pattern
								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   len(a		), len(a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if char == self.pattern[i]:
																						return i
								return -1







	def   _lowerCamelCase							(   self						:str	,  a						:int		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if self.pattern[i] != self.text[current_pos + i]:
																						return current_pos + i
								return -1







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					list[int]:
								# searches pattern in text and returns index positions
								__UpperCamelCase      :  Any       =   []
								for i in range(self.textLen - self.patLen + 1		):
															__UpperCamelCase      :  List[Any]       =   self.mismatch_in_text(a		)
															if mismatch_index == -1:
																						positions.append(a		)
															else:
																						__UpperCamelCase      :  Any       =   self.match_in_pattern(self.text[mismatch_index]		)
																						__UpperCamelCase      :  Dict       =   (
																						    mismatch_index - match_index
																						)  # shifting index lgtm [py/multiple-definition]
								return positions
lowercase    :   Any						=			'ABAABA'
lowercase    :   str						=			'AB'
lowercase    :   str						=			BoyerMooreSearch(text, pattern)
lowercase    :   Union[str, Any]						=			bms.bad_character_heuristic()

if len(positions) == 0:
		print('No match found')
else:
		print('Pattern found in following positions: ')
		print(positions)